class_docstr: Augmenter that leverage semantic meaning to substitute word.
  
  :param str lang: Language of your text. Default value is 'eng'.
  :param float aug_p: Percentage of word will be augmented.
  :param int aug_min: Minimum number of word will be augmented.
  :param int aug_max: Maximum number of word will be augmented. If None is passed, number of augmentation is
      calculated via aup_p. If calculated result from aug_p is smaller than aug_max, will use calculated result from
      aug_p. Otherwise, using aug_max.
  :param list stopwords: List of words which will be skipped from augment operation.
  :param str stopwords_regex: Regular expression for matching words which will be skipped from augment operation.
  :param func tokenizer: Customize tokenization process
  :param func reverse_tokenizer: Customize reverse of tokenization process
  :param str name: Name of this augmenter
  
  >>> import nlpaug.augmenter.word as naw
  >>> aug = naw.AntonymAug()
code: def skip_aug(self, token_idxes, tokens):
      results = []
      for token_idx in token_idxes:
          # Based on https://arxiv.org/pdf/1809.02079.pdf for Antonyms,
          # We choose only tokens which are Verbs, Adjectives, Adverbs
          if tokens[token_idx][1] not in [
              "VB",
              "VBD",
              "VBZ",
              "VBG",
              "VBN",
              "VBP",
              "JJ",
              "JJR",
              "JJS",
              "RB",
              "RBR",
              "RBS",
          ]:
              continue
  
          # Check having antonym or not.
          # TODO: do it again in later phase.
          if len(self.get_candidates(tokens, token_idx)) == 0:
              continue
  
          results.append(token_idx)
  
      return results
  
file_path: nlpaug/nlpaug/augmenter/word/antonym.py
imports: from nlpaug.augmenter.word import WordAugmenter
  from nlpaug.util import Action, Doc, PartOfSpeech, WarningException, WarningName, WarningCode, WarningMessage
  import nlpaug.model.word_dict
label_desc: Use image node_id 2 for calling the AntonymAug obj's underlying member method code with example usage: obj.skip_aug(token_idxes, tokens) and returns: results